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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Time series to irradiance tutorial"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import sys\n",
      "import os\n",
      "#sys.path.append('..') #append the parent directory to the system path\n",
      "import pvlib\n",
      "import pandas as pd \n",
      "import matplotlib.pyplot as plt\n",
      "try:\n",
      "    import seaborn\n",
      "    seaborn.set_context('paper')\n",
      "except ImportError:\n",
      "    pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datapath = os.path.join(pvlib.__path__[0], 'test', 'data')\n",
      "fname = '703165TY.csv' #Use absolute path if the file is not in the local directory\n",
      "TMY, meta = pvlib.tmy.readtmy3(filename=os.path.join(datapath, fname))\n",
      "TMY.index.name = 'Time'\n",
      "meta['SurfTilt'] = 30\n",
      "meta['SurfAz'] = 0\n",
      "meta['Albedo'] = 0.2\n",
      "\n",
      "# create pvlib Location object\n",
      "sand_point = pvlib.location.Location(meta['latitude'], meta['longitude'], tz='US/Alaska', \n",
      "                                     altitude=meta['altitude'], name=meta['Name'].replace('\"',''))\n",
      "print(sand_point)\n",
      "\n",
      "# TMY data seems to be given as hourly data with time stamp at the end\n",
      "# shift the index 30 Minutes back for calculation of sun postions\n",
      "TMY = TMY.shift(freq='-30Min')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SAND POINT: latitude=55.317, longitude=-160.517, tz=US/Alaska, altitude=7.0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Retreive required parameters"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate the solar position for all times in the TMY file. Requires either PyEphem or a compiled spa_c library."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "solpos = pvlib.solarposition.get_solarposition(TMY.index, sand_point, method='pyephem')\n",
      "solpos.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>apparent_elevation</th>\n",
        "      <th>apparent_azimuth</th>\n",
        "      <th>elevation</th>\n",
        "      <th>azimuth</th>\n",
        "      <th>apparent_zenith</th>\n",
        "      <th>zenith</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1997-01-01 00:30:00-09:00</th>\n",
        "      <td>-54.750103</td>\n",
        "      <td> 328.861096</td>\n",
        "      <td>-54.750103</td>\n",
        "      <td> 328.861096</td>\n",
        "      <td> 144.750103</td>\n",
        "      <td> 144.750103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1997-01-01 01:30:00-09:00</th>\n",
        "      <td>-57.530205</td>\n",
        "      <td> 353.265280</td>\n",
        "      <td>-57.530205</td>\n",
        "      <td> 353.265280</td>\n",
        "      <td> 147.530205</td>\n",
        "      <td> 147.530205</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1997-01-01 02:30:00-09:00</th>\n",
        "      <td>-56.627712</td>\n",
        "      <td>  18.751764</td>\n",
        "      <td>-56.627712</td>\n",
        "      <td>  18.751764</td>\n",
        "      <td> 146.627712</td>\n",
        "      <td> 146.627712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1997-01-01 03:30:00-09:00</th>\n",
        "      <td>-52.329549</td>\n",
        "      <td>  41.456666</td>\n",
        "      <td>-52.329549</td>\n",
        "      <td>  41.456666</td>\n",
        "      <td> 142.329549</td>\n",
        "      <td> 142.329549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1997-01-01 04:30:00-09:00</th>\n",
        "      <td>-45.717933</td>\n",
        "      <td>  60.026503</td>\n",
        "      <td>-45.717933</td>\n",
        "      <td>  60.026503</td>\n",
        "      <td> 135.717933</td>\n",
        "      <td> 135.717933</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "                           apparent_elevation  apparent_azimuth  elevation  \\\n",
        "Time                                                                         \n",
        "1997-01-01 00:30:00-09:00          -54.750103        328.861096 -54.750103   \n",
        "1997-01-01 01:30:00-09:00          -57.530205        353.265280 -57.530205   \n",
        "1997-01-01 02:30:00-09:00          -56.627712         18.751764 -56.627712   \n",
        "1997-01-01 03:30:00-09:00          -52.329549         41.456666 -52.329549   \n",
        "1997-01-01 04:30:00-09:00          -45.717933         60.026503 -45.717933   \n",
        "\n",
        "                              azimuth  apparent_zenith      zenith  \n",
        "Time                                                                \n",
        "1997-01-01 00:30:00-09:00  328.861096       144.750103  144.750103  \n",
        "1997-01-01 01:30:00-09:00  353.265280       147.530205  147.530205  \n",
        "1997-01-01 02:30:00-09:00   18.751764       146.627712  146.627712  \n",
        "1997-01-01 03:30:00-09:00   41.456666       142.329549  142.329549  \n",
        "1997-01-01 04:30:00-09:00   60.026503       135.717933  135.717933  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# append solar position data to TMY DataFrame\n",
      "TMY['SunAz'] = solpos['apparent_elevation']\n",
      "TMY['SunZen'] =  solpos['apparent_zenith']\n",
      "\n",
      "TMY['HExtra'] = pvlib.irradiance.extraradiation(doy=TMY.index.dayofyear)\n",
      "\n",
      "TMY['AM'] = pvlib.atmosphere.relativeairmass(z=TMY.SunZen)\n",
      "\n",
      "DFOut = pvlib.clearsky.disc(Time=TMY.index,GHI=TMY.GHI, SunZen=TMY.SunZen)\n",
      "\n",
      "TMY['DNI_gen_DISC'] = DFOut['DNI_gen_DISC']\n",
      "TMY['Kt_gen_DISC'] = DFOut['Kt_gen_DISC']\n",
      "TMY['AM'] = DFOut['AM']\n",
      "TMY['Ztemp'] = DFOut['Ztemp']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-4-0523b848c9cd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mTMY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'HExtra'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpvlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mirradiance\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextraradiation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTMY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdayofyear\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mTMY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'AM'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpvlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matmosphere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelativeairmass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTMY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSunZen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mDFOut\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpvlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclearsky\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTMY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mGHI\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTMY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGHI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSunZen\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTMY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSunZen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/will/git_repos/pythonic-PVLIB/pvlib/atmosphere.pyc\u001b[0m in \u001b[0;36mrelativeairmass\u001b[1;34m(z, model)\u001b[0m\n\u001b[0;32m    227\u001b[0m   Expect={'z': ('array','num','x<=90','x>=0'),\n\u001b[0;32m    228\u001b[0m       'model': ('default','default=kastenyoung1989')}\n\u001b[1;32m--> 229\u001b[1;33m   \u001b[0mvar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpvt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mParse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVars\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mExpect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'kastenyoung1989'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/will/git_repos/pythonic-PVLIB/pvlib/pvl_tools.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, dct, Expect)\u001b[0m\n\u001b[0;32m     78\u001b[0m     '''\n\u001b[0;32m     79\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdct\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExpect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_fcn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mExpect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mparse_fcn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExpect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/will/git_repos/pythonic-PVLIB/pvlib/pvl_tools.pyc\u001b[0m in \u001b[0;36mparse_fcn\u001b[1;34m(self, kwargs, Expect)\u001b[0m\n\u001b[0;32m    196\u001b[0m                                     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Error: Numeric input \"'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' \" fails on logical test \" '\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'\"'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m                             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 198\u001b[1;33m                                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlambdastring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    199\u001b[0m                                     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Error: Numeric input \"'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' \" fails on logical test \" '\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'\"'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Perez"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "models = ['Perez', 'Hay-Davies', 'Isotropic', 'King', 'Klucher', 'Reindl']\n",
      "TMY['Perez'] = pvlib.pvl_perez(SurfTilt=meta['SurfTilt'],\n",
      "                               SurfAz=meta['SurfAz'],\n",
      "                               DHI=TMY.DHI,\n",
      "                               DNI=TMY.DNI,\n",
      "                               HExtra=TMY.HExtra,\n",
      "                               SunZen=TMY.SunZen,\n",
      "                               SunAz=TMY.SunAz,\n",
      "                               AM=TMY.AM)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "HayDavies"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY['Hay-Davies'] = pvlib.pvl_haydavies1980(SurfTilt=meta['SurfTilt'],\n",
      "                                            SurfAz=meta['SurfAz'],\n",
      "                                            DHI=TMY.DHI,\n",
      "                                            DNI=TMY.DNI,\n",
      "                                            HExtra=TMY.HExtra,\n",
      "                                            SunZen=TMY.SunZen,\n",
      "                                            SunAz=TMY.SunAz)                                "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Isotropic"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY['Isotropic'] = pvlib.pvl_isotropicsky(SurfTilt=meta['SurfTilt'],\n",
      "                                          DHI=TMY.DHI)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "King Diffuse model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY['King'] = pvlib.pvl_kingdiffuse(SurfTilt=meta['SurfTilt'],\n",
      "                                    DHI=TMY.DHI,\n",
      "                                    GHI=TMY.GHI,\n",
      "                                    SunZen=TMY.SunZen)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Klucher Model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY['Klucher'] = pvlib.pvl_klucher1979(SurfTilt=meta['SurfTilt'],\n",
      "                                       SurfAz=meta['SurfAz'],\n",
      "                                       DHI=TMY.DHI,\n",
      "                                       GHI=TMY.GHI,\n",
      "                                       SunZen=TMY.SunZen,\n",
      "                                       SunAz=TMY.SunAz)                                "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Reindl"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY['Reindl'] = pvlib.pvl_reindl1990(SurfTilt=meta['SurfTilt'],\n",
      "                                     SurfAz=meta['SurfAz'],\n",
      "                                     DHI=TMY.DHI,\n",
      "                                     DNI=TMY.DNI,\n",
      "                                     GHI=TMY.GHI,\n",
      "                                     HExtra=TMY.HExtra,\n",
      "                                     SunZen=TMY.SunZen,\n",
      "                                     SunAz=TMY.SunAz)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yearly = TMY[models].resample('A', how='sum').squeeze() / 1000.0  # kWh\n",
      "monthly = TMY[models].resample('M', how='sum', kind='period') / 1000.0\n",
      "daily = TMY[models].resample('D', how='sum') / 1000.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = TMY[models].plot(title='Modelled in-plane diffuse irradiance')\n",
      "# ax.legend(ncol=3, loc='upper center', title='In-plane diffuse irradiance')\n",
      "ax.set_ylim(0, 800)\n",
      "ylabel = ax.set_ylabel('Diffuse Irradiance [W]')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ib3ZHUiqgZH7oTkDJ7vCGlPIYS6EMBY6WUk7DnPw/XQhRDMwCpgFPWJ8V3aSl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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f50b797fd50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY[models].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Perez</th>\n",
        "      <th>Hay-Davies</th>\n",
        "      <th>Isotropic</th>\n",
        "      <th>King</th>\n",
        "      <th>Klucher</th>\n",
        "      <th>Reindl</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 4531.000000</td>\n",
        "      <td> 8760.000000</td>\n",
        "      <td> 8760.000000</td>\n",
        "      <td> 8760.000000</td>\n",
        "      <td> 8760.000000</td>\n",
        "      <td> 8760.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  126.020357</td>\n",
        "      <td>   58.782821</td>\n",
        "      <td>   56.830698</td>\n",
        "      <td>   64.360708</td>\n",
        "      <td>   64.622931</td>\n",
        "      <td>   59.137119</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   98.944032</td>\n",
        "      <td>   87.587632</td>\n",
        "      <td>   85.451560</td>\n",
        "      <td>   91.309309</td>\n",
        "      <td>   95.026610</td>\n",
        "      <td>   88.152787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.715740</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>   58.800900</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>   96.567972</td>\n",
        "      <td>    5.616401</td>\n",
        "      <td>    5.598076</td>\n",
        "      <td>    6.155411</td>\n",
        "      <td>    5.667071</td>\n",
        "      <td>    5.622082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  173.452541</td>\n",
        "      <td>   89.248955</td>\n",
        "      <td>   84.904156</td>\n",
        "      <td>  103.102595</td>\n",
        "      <td>   98.899346</td>\n",
        "      <td>   89.601183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  558.131136</td>\n",
        "      <td>  531.675901</td>\n",
        "      <td>  526.219164</td>\n",
        "      <td>  533.076491</td>\n",
        "      <td>  533.061174</td>\n",
        "      <td>  535.251475</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "             Perez   Hay-Davies    Isotropic         King      Klucher  \\\n",
        "count  4531.000000  8760.000000  8760.000000  8760.000000  8760.000000   \n",
        "mean    126.020357    58.782821    56.830698    64.360708    64.622931   \n",
        "std      98.944032    87.587632    85.451560    91.309309    95.026610   \n",
        "min       0.715740     0.000000     0.000000     0.000000     0.000000   \n",
        "25%      58.800900     0.000000     0.000000     0.000000     0.000000   \n",
        "50%      96.567972     5.616401     5.598076     6.155411     5.667071   \n",
        "75%     173.452541    89.248955    84.904156   103.102595    98.899346   \n",
        "max     558.131136   531.675901   526.219164   533.076491   533.061174   \n",
        "\n",
        "            Reindl  \n",
        "count  8760.000000  \n",
        "mean     59.137119  \n",
        "std      88.152787  \n",
        "min       0.000000  \n",
        "25%       0.000000  \n",
        "50%       5.622082  \n",
        "75%      89.601183  \n",
        "max     535.251475  "
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TMY[models].dropna().plot(kind='density')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f50b79a52d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wqlLqPmAJ8KpSKlI75wLeAXx7R2/mcmnE48E6daWxXC59xvalmsuDaVLxBGgJ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\nK3NZvHAVbx2ZjLuzgur9q/H7PZEOW4gRJYVJDEt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       "text": [
        "<matplotlib.figure.Figure at 0x7f50b782fdd0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax_daily = daily[models].plot(title='Daily diffuse irradiation')\n",
      "ylabel = ax_daily.set_ylabel('Irradiation [kWh]')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AY6YtYBA0Et/UEn9lVKhqq0vXNoqtfUGt8BC3VasD0FQBrRTGZNtWuTnxZxOU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olAXZHQ55FJxPM86Vg3fiDhj51WGKttHpOQZZyDt1pYWLMECUcfvh/5UshJwVjiwGwx\nfMakAlMaWXzcNynuvmcjdBzuZGmyXYNF9OSM4vuOqTh4DIOe64ZHz1UIcGiUhEhqkxcy98rbMPeK\n26abq7FrQSmN1XMW+mw9EjdVhoM/9hMQM7PY8z1vQ+vYtSgOHcbht38vWscmk6LWJQ6zzrXsPDl3\nm/R0yfb46ee1/iUOfv64OYh1zEFAYW7BGl+CBbi5NBkc5bqJwCIMIQoAgPKQ4X3lWvniUVcTAth3\nQRAsPXOAnTFz1xHkPgkhk1GnKuUViVPsYR6aDAqqOxsXuoqSmlqPUSTRXvfxr57CkOrAnEfCkYaY\n6x6JVSQaJXlsn7j7FO55ZAVPR37rL+7FN05c2PW46TNxz5dEoVpfYMQCTUb72DFk9z0IAJBUmes8\nAYdpByxFRQ7uF5JSpXVTcze+9tBcJ3VFkkHCGMB49JZtKSuNTWqXhCJzFewx/NciYgKaK1hZyWXa\n6zOo1ZHQ4pYVVJQGzcY1KwJ8A53KUaT4SmyntDQW73wjlq7v7AgOdR4J26WhUSzDejgDqSLZ/+1v\nxNmPfgwAUBw8iMWHv4LHX1Ti8hMPJQqCaQtIyJeX8H2/9q9hOMeZq45C5xmyzptw3We/hGZ/Y2Lt\nxW7COQdUZMUiRd/EGxgILaS1gYDdEAQUMsp7MGOgOYeAQp9Yd2M6Gm4MBqRI5qkb4fxt3zr1XJ2i\n0krj3JOWDFCDQbCwuRcHDuDo//Ur8QXish/4/qnIGp3EoIoF8mTcObIxG+M4yaLH36gZIA5+/oix\n3tcerGJuZhkYGAgG39JAaoEcoV1uCDG6dy60orYtGYh7zdjshgGg4tAWOIrc/ibfQZHEecn4jRTG\neiRSSftOZfDrQYLYnrX2zAD2Guz5FrZWsD5LNVexxzsGBTeSbPeN/JxHUqGS4Z2v08gLJiG4wer6\nM9eG9+8fOo+rDs/jxVfsbLxd8orE3w7G0t3WJQEXFz0ZodISRjT9l7bYzC7eSmo0i4Cm0apKrQZj\nGwHFBHCq5rAVqKB0CivUWlnLQMCjVcBcwVj4LTOBEI4BI4okWdoMiWvuq6alhI7osicpEldfUtU8\nkkw4an2DpTe+CfPLsztuNKwosHDHnWhdO71Vu1FTHEWtxuOKH/sfMPfm77HfHbcDtMMAACAASURB\nVDyEG+75PA6U55BrBTEfQl/DdQnIEmJ+Aa2BneP1Gxvg7TayxSXc9ncfpRdzatq2RFy8nkdWnZ6Q\nI+Fk5QYLlFmeL+4KUO1nrZkcJRUAZpH1yaGxjRZgDJYWRvuC7DpXWjtKGZx7cgvL+2ZwDgwc023u\n04rbDJZWnsIMbRxOkeRTNmWKaXgaeYpE47X1mjGJYnYOr/rAn2D/ZfNEtGrD0YVH06UeiSbPDPRv\nW9hJ42uLuTKMJ6gtDYZGQf1huDMCx4W2JPSYLdGGzxjKsgxKDLaupJSV9Ui080jcXmTPt3frnFck\niaEyTpHUChIznSbbYSri7KL51jwSV8P1F596EL1S4LVXn8Rco4RtVHvxYoyB0mYqpXTph7aiuKGO\nQluuQCwXmUeDhIJE6k7HFZQLbdWaT2ld+RaZQAiVxYqknmzPIWkRu3lYht2KFo1TJDE6ywmjBHg9\nRxIaXEWhLcYgEyZapxjSOpIsmxzaAsgqijwSR2nOYEYLPsYIYwwH/vE7/3/23jxesqu67/3ufYaa\n7nx7HqTu1nA0ISEkISGhASQExoCFICE2BkOcvM/L8AyxP4nj2PGLE2PHxLEd8pwEx8Z27HiIk9g4\nYDDYYAw2NpMQMogDEprVGnq4faeqOsPe74+99xmq6t6+3X27dSXq9/lIt6tO1dnn1Dlnr73W+q3f\nIty166SfdZjPV2qv5QB3WEiJN2FW5f7cPE2t2Pf1B/Dn5mqhNl+AzFXtPREECCFLz2WDRXKj4PZb\n90jqhqSkDJprp6sxcVRBLQ9UDyE1+/evFM2lasQGZXJbASmTM/Uc0oaO1RbK5ZnxSLbvnKgZus3C\nJckKN3/ig3znH/wavvWcnOrxRn3SsHLeQTDAqhu452SSIpstovvv4cLOvFGb1pqeahRG0uRI7NfN\nq0J+xEzgGiGcR2JYfRojJ6QRlnElCa0hcerVI3MkWflsV+HC3f00Kai4Lqzd7/VQWL26iiaaq1PZ\n024wvXrMfsc+x8pD6LU9EunBqy5+CGn7GxWdFXXGMVXeP9U6kixXtKwhObG8zNETXV5x4aNcu/+p\noXE2ilxpQi/bUAOtrW9I3F8hKzkSUTz0oR8UK8NcZZa1ZRCIrDAGrkCoKCpUacFHB4pGO1XWyWBo\nKyAzsdfKvZblWUHDqxqSkTkSyhxJqUMkKv8vz7l28ezGdJA3vkbrU7cqTLPBZHtpSDaim3Q6ePnM\nHt7x/vcQWF20tSjKYCbzcNcudJLQueLK2rZti0fY8XSday98HzxZa0V7ughkKfjpULRm1a7LYZlX\nc6EM8zmjleVCfp7K0FJwwf7Pg+t3Xrk2nl2whDol7AwXfp4MAk0uzEr8+NEVDu75SMFO3Ezsac9w\nwTf/Bj/L8OxxZpYkEA5V1o9GUG3FPOCRDC1ejvWQNhcjfJ9Os822Z57kKbm9mERr9F+XK7TW2qgs\n54XPXfQ0EYI8NzlPaY2w8Nx84dpLDCNZQ03XEQiMgnB5XErl9JM+GRKh8lpoy9GLJzsdfvjW65lM\nl4qcZ5YFsI4hCULFTQefYKplciK6YkhO6JI8oSuimP00J/Cswev3WFzdWE5rPeS55h/ddA8z3kMn\n/ezzwJA4d69SFCaK/yF9v5g4jfJqOQGohYRU2VandhJ2+QOVpzUuubux/MotNpQjEZlZkVQeiEzl\nZPbmyih7vquBFaNjnLjQVknddAdwco8kGUh4DiYzHbyi2Zc51oJ8IOsS7WcDLjfgktjrGRKAYOdu\nACavubb2/stWlnl5/GWzz04HPA8RhEXe5oyPU1rq7lLZ36EIWaoBQ+Ko265+Qdoqe+EmsJyn2c7n\n9BX4oblu1bCjK5oL86SYOE/pWO1kmPQzAq+HJ0wTtg2I8Z4SRNX4FYbEvNfeQL92gDAoPxeG9ftz\ncPEiVY4IAmS7jTcxifB99j72IE9520G4WpGqZ2j+ZLkhvRTMSFfUq2yORJRei6dM0bF7lh2TzD0B\nuVKkRevt0YbEzQlplqDs5J1jlCPS1BQ6StvTp/BI7HeCIo+mCo8k6XkjyQuDOdHQLw0IgCCjp0sv\nr9oio5/k+L55vXdqkQu3rdvlo4ZHnlri2OJwrVimFBONBE+fPM+25XMkRbJdiMLsaVEyZ/wgLGit\nua6ztmQ/YWW5BbOlGqczJKgUXVV0tCvJ6jQ1mCNRWlo2SOUzeVZbGYBdrSCpqtJWQ1vO/TbH4zwS\nXft+lVHmvK/B/gPeGj20A2swVJ4hQr8MbUkfSM6qIcmXTTLYd+1F5fq3WPPAAXoPPjCUh/G9gNzm\ndA699+dAa/pPPF5Y3p3v/H68idOvJwl887s0jq7ABTah6jxe55HYz7pFQK+bMjHVNCGVSkW8C1Pe\nqy/hikZsjr9i5J0haSRdZPvUDYm7d1aX+3Q6q3wiv4GUAL1JCdViHGssRBgiGiYXlwpzHq0NStGH\nFS+kMRjaGoDyfIQQnP+vfhJ/agqVJOx77EHuvfYWTgTm2lbZae5pTDOTqzRy/Vn5vGtlny1Bkht5\nIJnnKLyCyj9IAPjQXz7CNx5b4J9+99Ujm11BWaGfZGkxeZtnVNFPuiZXk+dGfVy4vIy241UIGYVH\n4iPlqIJE+7sUhsTJ1OTF35ohqSyEu0mObz2Smw89hhqWPV8Tv/zhr3Hp+bN8zx0X195PM4UndY00\ntBaeB4akkoCqhbZsfkGIggarXSK8oNYqekGLw3o7OwdCW0Jn6MrpFwmuSrJwUKJE2VVptVgpz1Ut\n12I+Z/sxVJ5zp8irkYauXByjPc3K97UQ9ZvaJdcGOqKtFdpyuZM8UwiJoYFhihDN7s6eIckWTE9p\n55Gs3cfRYPZVr2b6ltuGPA0hZUk/tpNY64ILi+3TN93MmWDWa/Adf/hLzC2fYGLhOA+8/AYSWy+j\nlZu73OLCTALd1T4waZmBddUC82mJatu8SUU7y4VLp5aPnRIDrvr9HMnqco/GVMqX9UEAVpc2NzzZ\n2Lcfb2aGnd/7fYX30E08CKHd3JghqYazmicxPn3bMyiw9UNCK2aPmuLhrv39TFbEPituQZVnJGmG\n6wvvHB0X2tJC2K6CIZ7KSHVQeCSNsNwvwLHFHkdP9Ox+Uxjh8DpDkudpQQFXCPIsI+kb5XGp0pom\nmtt/VQLGGZIkDfCbJ/dIAi8n0X5hSDwyEhEi8hzteTXCQL+f8FUu5vF8F3c2P02mNn5vrHRTknTY\niDpRSM1oA1vFlg9tlTIhFRFFl8zGhBd8z60CbDy0mAAU3eYEH8zvIEsytNbE8iAfy29C6Kxe3WoN\nSLCWgBUuwVZfSeRqLY9EUO3HULK2BFJX477O5RoIbdW6xNnzGzBYa3okdgLO8sxyJt2DaCfmNc/w\nzDF9y21MXHNtaUhOMpjwPLz2iMp5T9ZCLZuNyVbAzqceZ+rCi7nqwD5c/3iwHokoV8ImmStYtWEw\nJ81RHGolhqk6Ze7OQVpJ98mFY6eVm5JWY2p5pYuaqix0JidOeV/roX3JpVzws7/AREXZuZ+ae2ai\nvbHcTuMUPJL+QKGrDEJae/ZCJURUq5exn8tVbotznURKuXB0RBKXt/RskaGLLrQa1pC4fEuu6NsF\n2qjaD7QunifTirdkjKVZTtKz0is6R6rKcdvr7AgH1S6baerjieHJedCQPC128Bv5XWRuPhM5fUKa\n3dXaOYDJi5zQkxzTMwSeohVsvOlVL8nJRni3RU8V/QIwJEVoq8baKlV0oYxHK6uwW7q65Y+TZila\nwwkxxbN6zrAmKheiuPDrxOCNtlY54YClDA4wQJznUf1xnf6V6Zwo8e0+GsHweMMeiV2JDVzQtei/\nLi6bZRoEhbRMka84A7bTyeBPT7PnH/xjpK3wnuycOksJYOYVt7Ptrjdu5qHVUHQXbATsfOvbitCD\n1roS2nITlPm7bHW0TEV1JbRVYc+5lXQjLCfRVc9MwhPHF07rWN3aZml5lbQTgFZccc9nuKJ3evs7\nFbjw4lR7Y2FE54lJrQga6ysipCMMzeSLrsTXWWVlL4Y8kjxPSVMjYCqULupfPFufVQ0NeyqrdTps\nNeuhxbY8ysEZw2xSI6RFhNJIJ92issIL0EhUlpGmvSJHUi1MdQhtKK0IbStBlnlFGGpgtOKcAVZo\nkRIUwrXOkLRss7eqR5KkPTL8sgRhg1Bac97UszTlcNHhC8qQOC9Ei5KpZf5deh4ulCNEPUnaa5Q3\nTZ5npgpdSFJ8U6VezUsUkt5eEQoahClKMsKQxX5VPsKQ2GR7ZaUqtS4YZKGXF5ODV9QiVDPzdUNS\nrFQGYpX+Wh6JXQ1rVL0g8Rwk2x2kbYscrCOPvh6a5x8YYnJtJoQNu4S7TLLfTQJaa5TSNdKGmxtW\nuonZLuoKCKthuVrvSuNdhZXV9pIwnsPMYpnYPxU4Q7baTeg1Gkwli1z7uU9xaWd0c7HNRG6vX2eD\n+SgXzpLkJ/Uo5448PvRe+4oXGUkS6dnEdrVVtPmbJBn9JCnqSNx6yhelXI1rXOXlKUp4hcJFp2mu\nj5tL9nUe5uXnG1bSYIgazCLMtwOrSn7UiEcazyi3nlG1b4wzKG5BUUQklCDPJb7MhzSxBvuRlG0p\n3PnlJCKg1VuuvQ+QWAX0lGAoPzKK6uzQT3LuvOQh9ne+NbSt7Hdy8hzJ1jcklWT7YIFesar0HKXP\nTgD2u6teaZ3zLCsSdCkBUtaT5kVviMAbauIT9swE4OKi1euUK8VgcVOujedRfYwEuqhp8T1JaFkp\njXA4b6GEqIWxytBWWdcA6+RI7O8hhCUfOI/EO3eGxE20+WZTizYJwdw8+//Fjxdiia5fTJ5ro15Q\nWSy49UCv2zfhjYHQ1kJmJidPZyxbgb5WxSNJcA2bTo8c4H7BXr/PkjfBzIph5Dhiw9lEZ8msVL0N\nhraaoROyVOsakr/9G/+BO+/73PD3Dxw0kiRCotx1GFBmyLK0mMCF1vS79rdWspRIsWGqwOZGe/Ya\nuBBdSb7PCTwjdDjKI5GqVCXOKyFsE53IUFnfdoN0Hon1au3t48gHLkeqlCRXHoGvOLGc8IEP38/x\npX7BQjP7dobEMsyEWwgazbZm5kKs5T2aWkMCkA6kvvM1FsZgwlodP0P2hyVbijqVDSTbt+ZTXkFV\n5qAaf6x6JEERWrAS1vbGu+Oev2TbUdPVME+tIRGGCihFXmvD6/bt+0GpMmzxyj/+PQ4+9g3jkWR5\nXUJ6gMUF5Q0ga9XNJaQQSO08IHfR6zkSpeqvzW9gz9fq9KxF/3UV7J40qcqi+PEcGpK5hWfsWM9N\no52NoHXoUFGY6JRbVa4K0oa7ei6k0u2m5JmuiAUaTD1s5ODnkmOsWEPSrHjD00cfA2By//7TOs5C\nqSDtscAUM0tHCXftpnPlVae1v1PBa//yI7zuf31gw7Tlpg1n+TovyBKj0F5dJlwd9tBEEOCrjFxI\n0qTe4dORavI8Jask21XulJXL0FZpSMyz0tVNpMoJW9YjKUbMrdBhbujyAwiyXumRaFU8gwpBmueo\nPCXDw0chlPOGdOHGNqp6bEi6qsE9uy+nJxt8/dHjfOa+w/zUb3wRpUvFjCGPBJPL8bycVPg0RlTF\nZ2lSeF3pQHhrvcr0XmKEHuUIb8zlmcQLIbRVZW2VDWQsa8s+zMXKXJiL4EIBF7/yVRx41OhwJXlK\nlpXMCS0HmtPYSd/3Tdy1ivbqIkHSH5ls10oXKwYHZ0iqrXCrXo7nicLIVGU2in0KUVTQutdQRtxC\n63L63mhD4rj8MjBsH1lU9Y8Io50lXP/049z1u/+FA1Pbz/pYm4FyxahRqs7Mc5ex309Lj6RyfV70\nbJ93vP89tPuuql/TqvQXec3nPsX3fODf0diz97SOrezjkbBKk1Z3mfP/9XtoHbrgtPZ3Kugsr7Dt\nyOEN05YbjSamj3p+UmKB6g3XLghhFLhz6dHtplQVBsqkd2YowNYTaDTNtQh8yjoS5Wp3nEfSQOY5\nfrNtFpr22Lw8I/Ry+qkqGFlVSJUVYpK60lJZI1BZhlYJmfYJhSraN6SZKnTAXIjT5Ui+wQFONKd4\nTOzm8FETojq62OPRp5dHGBJriKUw+5QeCEFLVmtsDLKsV1CcB/Mk6Rq0ZoBeP8X3VaEOUIUj/Mhs\ndH1NFWeNFhNF0W7gw8ClQCeOY1XZtgf4TaAB/Hgcx3+61n7KH7fsoa6ESWaXHklV46r8cSevvY70\nWUMnTPO+yWc42+kBFYqcclIQno/Xz2riiF6u8LRlf2T1ZLtJ8NfhXMwqldirSIVIIUtZh+IzVQ9E\nFmEsUd2/tTZ+0odworjBB+EMq/Q0fR0yabv2eZ7pSncuPJKg2WJm4cFae9+tDFEo7GqbZKx0INQu\npNIjt4uRqkdyqK1YBtqrSzBl4vR+pfp+YvEEeZoQ7tlzWsfm7iMdaEvi0CdXbt4kaDvZy8Eq9TXg\nhQ0kqqjmXwvz3/VG2pe/aPQ+bKO6lZU+VSkY10hMaWUiDJbQkhf5RlHoorlqcN8exzJtgizFazQQ\nFT/HJ8P3NP0kHdl+18tzwsCD3MQ73JOjkWRZgtYpmfAJhS6kbJI0tcoDqlDOFlqhtM9TYgdgdPse\nPrZYjLPSS2tzHZgQOYCWgiRTZDZ60fFdZEZyfKnP7GSDPEvIAmO0Bg3JWvUxAL1el0D7Q4thgNx6\nPuI5pv8ew6iF/dWIbf8c+FHgTuDH1tuJHuGRaCELiw/UJtTBinL3EGZZVrTJBJtcryyY3L59z8Mf\n6BXQuPSSYt+DF0XpfIgE5VzMoGI8qpLvnhRF9XnJpKqcM6JIrJsaBEeUt/vq9u15r8Xasr0ofNM/\nO3T9o533cy6S7S5HcPaH2hQUrJpclSuxoubIJVtHeyTpkSMAtA8byW6Fh6xUeOe2tibYuXHNsiqK\nnJd1crxNqu7fCAJLRtgoRBDg69GhEodtTz/B/Ou/i9ahQyO3e3lKLjxWV7vGkLieKLa4VavcijKa\nxla5NfSelY3RQhRGIbSh1RXdJkh6VtZfV5hQVicv6Y3smii0Kjx/5WjhFlmeFQn60DMh5BxJv5eg\nhc2bVJ65HI9npCFIpAQcWzjKW6/5OlONPr1eVa14MEciTb94K1czZbtKKgE/9It/wUOHF1FZmSNJ\ndECWlx5xlq0tl9Lrd/mj/FYeaJ0/tC1P3XPwHOZI4jjux3G8Fj/xijiOPxvH8QqwFEXRmlnIIqwz\nkGx3Fh/K3tkutFWdKJ10dZalRqnXTgq5lHXPwiXbQ7+QqHeYfP3ryCZaRYKt7pHUXUwok11VQxJW\n8yXSGw5t1ZLtpUfi2riaF3YVlaUIpfDWYEQ5D0142hiStGz4I9DnJLQVbDPNp9ZjjGwlOAkLpTT9\ngvZo/rTs9dSBpt/LbJK3vEdm73gVIgzZffhwub8Ko86fM5PHqfR1qcIle3XDUtSbp14df7rY9+4f\n4rwf/4kNf14EpuulzEYbkr/1m+/j1R/67+vuw88zUu3Ts4bEXYeyMZsqW2MLXTwrnidLORv7XmiN\n0DJtwsS1QS5DW26S7PW7I9c8UuWFh6/RtcVnkqdFziT0PWPUkPSTvi1PqBStKsUSE4VSQIrPfONZ\nLtp2hDde+Q16yYi2EZX5LslUIVczMzlpPGg7t33gw/ej8qQ0JAQsLpXkiGwdj6TfW2WF9lBeBSr6\nhBtYDT5Xle3VJdUJYAYYoqB4XoXyizReg524Hc13ZqbNiZkJeHYZpK3fEDAzY5JqzYY9RalptYLC\nGOXCLwn6mFUkwNREC6nqsdvZ6U7RPCsIvFr8XErqPDxKQ9JphsVxtBrlhWo3gyLu7nseMzPt2sWq\nysF4lk00M9PG9Yzy8wShFDNzE8gR4a1sZRqeOorwjSFpyPL3cI1EZ2baeJ4s3t9sTL3zbSy86DLm\nLj5Qe/9sjnkyrDe2WaVKOu0GiW0gJIX53XdYJVwamuUT/SK05fY185o7OO81d3D/v/13Jr7v+czO\nTRRjvui9P022ukrrNM+7YxPdua05mpqa2tBvuCm/9Uwb9u04pa94qymin4wcu7NiHvP1jstTGX18\nsKV4wj7PrVYDeoDQRV1WIAWZfZiaDb/oJuke0Y59/jN8gqRnnzXj5c/MtPGkYzTWjQSAzDOkypme\nnoBVzEK1smj0pEZ45vuddgO5Yoyb75twlKS8RySapDLdpjpgrm1CWwfnTvCk1y2Lr3XdI1HCI2z6\n9EUIGvbs2YU8qgmbmu8670k+/vR+JFkRCUkIuPe+Q+zeOcklFz9Mu+2t+XsLTD2Osvd6FVbVHynU\nSe+j58qQVJf8U8DIFlx5XmEyCIFyMUNX8Kc1CwurJH3Xtta6hfZ9+2EA0iThxOJKYUiUqHskufBA\nQ5bq2moToLtq9HxyJKsrK3XaXa5QA35dUfuiKI6jysLKM42yqwQhhPlMxegrIYsQmvNIFhZWC8Pn\naaM0emKxNzJW3utZ6q3noW3xozsOd+QLC6vMzLRPqfHRqUJedtXQ/s/2mOthvbFdaGthYZUFV/Bl\n76NJ25SKhuArX3wcFQVIpYb25c3M8Ld+830cm5ln4bqfLMZcIYR2SP80z1srwCsNSTtobug3fK5+\na6kyhMrXHXu9bV5uJsWlxWV0e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Wmh6Fo1JJe3p/F1yrPCkCNOt4Xw6eLln/lj/KTPtmPP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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f50b799e050>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax_monthly = monthly[models].plot(title='Monthly diffuse irradiation', kind='bar')\n",
      "ylabel = ax_monthly.set_ylabel('Irradiation [kWh]')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mpjzuv3c1ERGRjOsugdwO/At4HnjB3dWMJSIiQPcJ5GhgErA3cLGZrQO8DDzv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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f50b79ba810>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yearly.plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f50b7712e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f50b7668e10>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Compute the mean deviation from average for each model and display as a function of the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_yearly = yearly.mean()\n",
      "yearly_mean_deviation = (yearly - mean_yearly) / yearly * 100.0\n",
      "yearly_mean_deviation.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f50b5a5dad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f50b5a5abd0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}